Developmental Dyslexia (DD) is a neurodevelopmental disorder thought to reflect circumscribed neurobiological anomalies and etiologies, although the exact brain mechanisms involved have not been clearly identified. Along with environmental factors, these anomalies conspire to make the development of a certain complex sets of skills, particularly those involved in reading, difficult, and in the most severe cases, extremely difficult to treat. This project's overarching goal is to provide the first detaild and integrated neurobiological and cognitive (i.e., neurocomputational) characterization of DD treatment resisters, whose relatively intractable impairments are likely to be primarily brain-based. A related goal is to contrast this neurocomputational characterization of the DD treatment resisters with those of DD treatment responders and typically developing (TD) children. By systematically investigating the cognitive and neuroimaging similarities and differences in these groups, and the neurocomputational deficits that contribute most strongly to differential outcomes, particularly treatment outcomes will be identified. The project addresses important developmental questions by comparing younger (3rd/4th graders) and older (7th/8th graders) DD children, and will similarly study comorbid young (3rd/4th graders) children, using a multidimensional framework, that have DD and significant language and/or attentional impairments. In all groups, the project employs integrated pre- and post-treatment functional and structural neuroimaging, cognitive testing, online linguistic and non-linguistic learning experiments, and computational modeling. With these tools, a key goal is to develop and test neurocomputational theories in order to examine the relationship between cognitive deficits that generate noisy systems, rate limitations, limit capacity and consolidation failure (and therefore function as proximal causes of higher order reading-related problems in DD), and underlying differences in brain organization, and their impact on predicting treatment resistance.